The smart meter data of the Advanced Metering Infrastructure (AMI) can be tampered by electricity thieves with advanced digital instruments or cyber attacks to reduce their electricity bills, which causes devastating financial losses to utilities. A novel unsupervised data-driven method for electricity theft detection in AMI is proposed in this paper. The method incorporates observer meter data, wavelet-based feature extraction, and fuzzy c-means (FCM) clustering. A new anomaly score is developed based on the degree of cluster membership information produced by FCM clustering to differentiate normal and fraudulent users. We perform an ablation study to investigate the impact of key components of the proposed method on the performance by using a publicly available smart meter dataset. The results show that all key components of the proposed method contribute significantly to the performance improvement. The proposed method is compared with a set of baselines including state-of-the-art methods by using smart meter data of both business users and residential users. The comparison results indicate that the proposed method achieves significantly better detection performance than all baseline methods. We also show that the proposed method maintains a good performance when the detection time frame is reduced from 30 days to 20 days.